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The aim of this study is to develop a data fusion methodology for estimating behavioural attributes of trips using smart card data to observe continuous long-term changes in the attributes of trips. The method is intended to enhance understanding of travellers’ behaviour during monitoring the smart card data. In order to supplement absent behavioural attributes in the smart card data, this study developed a data fusion methodology of smart card data with the person trip survey data with the naïve Bayes probabilistic model. A model for estimating the trip purpose is derived from the person trip survey data. By using the model, trip purposes are estimated as supplementary behavioural attributes of the trips observed in the smart card data. The validation analysis showed that the proposed method successfully estimated the trip purposes in 86.2% of the validation data. The empirical data mining analysis showed that the proposed methodology can be applied to find and interpret the behavioural features observed in the smart card data which had been difficult to obtain from each independent dataset. [ABSTRACT FROM AUTHOR]

Travel time reliability is a key indicator of system performance and has become increasingly important in today's world as businesses as well as households require on-time transportation for their activities. In order to analyze travel time reliability, an enormous amount of traffic flow data is needed. Recently, the development of advanced traffic flow data collection systems has enabled us to handle that. This article aims to examine the fundamental characteristics of travel time reliability measures using the traffic flow data from the Hanshin Expressway network. Differences and similarities in characteristics (average travel time, 95th percentile travel time, standard deviation, coefficient of variation, buffer time, and buffer time index) are investigated on one radial route (Route 11: Ikeda Line). The result shows that buffer time and buffer time index profiles have a similar tendency as those of the standard deviation and coefficient of variation, respectively. Differences in characteristics among five radial routes in the network are also investigated in order to explore each route's characteristics of time-of-day variation of traffic flow. Additionally, the effect of traffic incidents on travel time reliability measures is analyzed on one radical route (Route 14: Matsubara Line). The results show that traffic incidents are the dominant factor for travel times in off-peak hours on the Hanshin Expressway network. [ABSTRACT FROM AUTHOR]

When we evaluate the performance reliability of a network, it is necessary to describe user's behaviour in a partially degraded network. This paper shows that the Stochastic User Equilibrium (SUE) model assuming user's route choice behaviour under uncertain network conditions can be incorporated in the performance reliability model. The effects of providing information are analyzed using the SUE model with two different groups of route choice; informed drivers and non informed drivers. It is found that providing information generally increases network performance reliability. This depends, however, on the probability distribution of the network states. [ABSTRACT FROM AUTHOR]

The article presents an introduction to papers on traffic engineering that appear in this periodical.

Probe vehicles provide some of the most useful data for road traffic monitoring because they can acquire wide-ranging and spatiotemporally detailed information at a relatively low cost compared with traditional fixed-point observation. However, current GPS-equipped probe vehicles cannot directly provide us volume-related variables such as flow and density. In this paper, we propose a new probe vehicle-based estimation method for obtaining volume-related variables by assuming that a probe vehicle can measure the spacing to its leading one. This assumption can be realized by utilizing key technologies in advanced driver assistance systems that are expected to spread in the near future. We developed a method of estimating the flow, density, and speed from the probe vehicle data without exogenous assumptions on traffic flow characteristics, such as a fundamental diagram. In order to quantify the characteristics of the method, we performed a field experiment at a real-world urban expressway by employing prototypes of the probe vehicles with spacing measurement equipment. The result showed that the proposed method could accurately estimate the 5 min and hourly traffic volumes with probe vehicle penetration rate of 3.5% and 0.2%, respectively. [ABSTRACT FROM AUTHOR]